Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • TrustInSoft Analyzer Reviews & Ratings
    4.3 out of 5 stars 6 Ratings
    Company Website for TrustInSoft Analyzer
  • Parasoft Reviews & Ratings
    4.6 out of 5 stars 152 Ratings
    Company Website for Parasoft
  • QuantaStor Reviews & Ratings
    4.8 out of 5 stars 6 Ratings
    Company Website for QuantaStor
  • Hyperproof Reviews & Ratings
    4.6 out of 5 stars 350 Ratings
    Company Website for Hyperproof
  • AlsoThere Reviews & Ratings
    5.0 out of 5 stars 1 Rating
    Company Website for AlsoThere
  • ManageEngine EventLog Analyzer Reviews & Ratings
    4.3 out of 5 stars 211 Ratings
    Company Website for ManageEngine EventLog Analyzer
  • QA Wolf Reviews & Ratings
    4.8 out of 5 stars 273 Ratings
    Company Website for QA Wolf
  • QBench Reviews & Ratings
    4.6 out of 5 stars 153 Ratings
    Company Website for QBench
  • Checksum.ai Reviews & Ratings
    5.0 out of 5 stars 2 Ratings
    Company Website for Checksum.ai
  • LALAL.AI Reviews & Ratings
    4.3 out of 5 stars 5,443 Ratings
    Company Website for LALAL.AI

What is Sulley?

Sulley serves as a robust fuzz testing framework and engine that integrates a variety of extensible components. In my opinion, it exceeds the capabilities of most prior fuzzing tools, whether they are commercially available or open-source. The framework is intended to simplify not just the representation of data, but also how it is transmitted and instrumented. As a fully automated fuzzing solution crafted entirely in Python, Sulley functions independently of human oversight. Alongside its remarkable data generation abilities, Sulley boasts numerous essential features typical of a modern fuzzer. It diligently monitors network activity while maintaining comprehensive logs for in-depth analysis. Moreover, Sulley is designed to instrument and assess the stability of the target system, with the ability to restore it to a stable condition using various methods when necessary. It proficiently identifies, tracks, and categorizes any issues that occur during testing. Furthermore, Sulley can execute fuzzing tasks concurrently, significantly increasing the speed of the testing process. It also has the capability to autonomously discover unique sequences of test cases that trigger faults, which enhances the overall efficiency of the testing procedure. Additionally, Sulley’s extensive feature set makes it an invaluable asset for security testing and vulnerability assessment. Its continual evolution ensures that it remains at the forefront of fuzz testing technology.

What is Ffuf?

Ffuf is an efficient web fuzzer created using Go, enabling users to perform scans on active hosts through various scenarios and lessons, which can be run locally via a Docker container or through a web-based platform. It includes capabilities for virtual host discovery that do not rely on DNS records, enhancing its versatility. To make the most of Ffuf, users are required to supply a wordlist with the desired input values for testing. Multiple wordlists can be utilized by specifying them directly in the command line, and when employing more than one, it is crucial to assign a unique keyword for proper management. Ffuf begins by testing the first entry of the initial wordlist against all entries in the additional wordlist, progressing to the next entry of the first wordlist and continuing this sequence until every possible combination has been examined. This systematic approach guarantees comprehensive testing of potential inputs. Additionally, Ffuf provides a range of options for further tailoring the requests made during the fuzzing process, allowing users to fine-tune their assessments. By taking advantage of these features, users can significantly enhance the effectiveness of their web vulnerability evaluations while gaining deeper insights into their applications' security.

Media

Media

Integrations Supported

Python

Integrations Supported

Docker
Go
JSON

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Free
Free Version

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS
Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

OpenRCE

Company Website

github.com/OpenRCE/sulley

Company Facts

Organization Name

Ffuf

Company Website

github.com/ffuf/ffuf

Categories and Features

Fuzz Testing

Not specified

Categories and Features

Fuzz Testing

Not specified

Popular Alternatives

Popular Alternatives

LibFuzzer Reviews & Ratings

LibFuzzer

LLVM Project
Accent EXCEL Password Recovery Reviews & Ratings

Accent EXCEL Password Recovery

Passcovery Co. Ltd.